11.2 To disclose or not to disclose, that is the question! A grounded theory of sports concussion disclosure in university athletes
Bibliographic record
Abstract
Objective Identify intra- and extra-personal factors influencing concussion disclosure in university athletes and describe their effects and interactions in an explanatory theoretical model. Design Qualitative research using Straussian Grounded Theory. Setting Semi-structured interviews. Participants 9 university athletes, 5 females, 4 males, aged 18–26, from three team sports (soccer, rugby, and cheerleading). Main Results First, we identified 24 factors divided into three intra-personal (Attitudes & Behaviors; Concussion Knowledge; Subjective Injury Severity) and two extra-personal categories (Socio-Cultural Pressures; Contextual Incentives) as determinants of concussion disclosure. Second, anchored around the core category Fear, we integrated these factors and categories into a grounded theory of concussion disclosure named Concussion Disclosure Theory (CDT). CDT posits that disclosure decisions are determined by the relative weight of two competing aversions: presence-aversion and absence-aversion. The factors identified seem to influence disclosure by generating one or both types of aversion. Our CDT also describes how most athletes adopt a non-disclosure bias strategy following a first concussion. Conclusions Our work highlights the benefits of using qualitative methods to study concussion disclosure and the importance of systematically investigating both intra- and extra-personal factors. Decision-making mechanisms proposed by our CDT can be used to generate future hypotheses and help design interventions aimed at promoting concussion disclosure. For example, it suggests that educational interventions designed to generate more presence-aversion could reverse the non-disclosure bias and promote concussion disclosure. Future research should validate the components of our CDT and their mechanisms of influence on the disclosure decision-making process in more diverse populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".